Evaluation and optimization of clustering in gene expression data analysis

1. Evaluation and optimization of clustering ingene expression data analysis A. Fazel Famili, Ganming Liu and Ziying Liu National Research Council of Canada Bioinformatics 2004

2. Introduction • Gene expression data - clustering of genes • Identifying potential clusters that contain biologically relevant patterns of gene expression • Measure of cluster quality

3. Existing methods • Silhouette value • Silhouette of a cluster is measured on its compactness and distance from closest cluster • Cannot identify proper clusters containing informative genes (illustrated in this study) • Neighbor divergence per gene • Needs external information • Uses scientific literature to evaluate whether a groups of genes are functionally related

4. Existing methods • Parametric bootstrap resampling • Requires generation of new observations through resampling • Time consuming • Cluster stability score • Cluster stability is obtained by clustering on a random subspace of the attribute space • Cannot work if two subsets randomly sampled contain independent information

5. Proposed method: Stability • Based on cluster’s immovability on partition • Immovability: rate at which the contents of a cluster remain unchanged during a clustering process for K = i to i+n (K –number of clusters) • Advantages • Accounts for all factors that affect the clustering process • Uses complete dataset

6. Datasets • Four datasets from previous studies in the literature • Three simulated datasets

7. Results for Yeast dataset

8. Summary of results

9. Simulated data results

10. Conclusions • Identifying meaningful clusters is important for further processing of data and to ultimately obtain meaningful results • Stability • Helps in identifying meaningful clusters • Also helps in finding optimal number of clusters • Improved performance compared to other methods • Does not require external information or subsampling